Field of the Invention
The disclosed embodiments generally relate to building systems, and more particularly, to determining RF sensor performance in distributed building systems.
Description of Related Art
The performance of wireless radio frequency (RF) devices, e.g., range, data rate, packet error rate, etc., inside buildings varies greatly from one building or location within the building to another. Building characteristics impact RF propagation. Existing automated solutions for analysis of the performance of wireless devices within a building include using visual inspection of building floor plans (e.g., a two dimensional (2D) or three dimensional (3D) geometric representation) and information about building material properties for predicting indoor propagation of wireless signals. (See U.S. Pat. No. 7,711,371, which is incorporated herein by reference in its entirety.) For an average user, it is difficult to infer building material type via such visual inspection, which can lead to severe prediction errors. (See Beneat et al., “Optimization of building material properties for accurate indoor ray tracing models,” in proceedings of the IEEE Military Communications Conference, 2004, which is incorporated herein by reference in its entirety).
Some existing solutions provide material calibration optimization methods that utilize wireless signal measurements performed in the building to improve the accuracy of predictions. A high number of possible combinations among selected walls and materials may be considered for selecting the optimal combination that provides minimum prediction error. Processing time increases in accordance with the number of combinations and fast solutions may be needed for design and installation applications that are performed on-site. Moreover, a manual process of assigning field measurements to respective locations on the floor plan can be tedious and error-prone. Furthermore, performance results that are technical in nature can be difficult for building automation installers unfamiliar with wireless technologies to interpret.
In addition to parameters related to sensor performance being site and mounting location specific, sensor performance, radio range and battery consumption are time-variant which can invalidate initial estimations and make long-term predictions inaccurate. Estimation of building system performance that takes building geometry, material properties, time variance and battery consumption into account can be tedious and time-consuming.
In a non-occupied building, radio performance is mainly impacted by the building geometry (e.g., walls), which can attenuate the radio signal and cause the radio signal to traverse multiple paths due to diffraction and reflection. Receiver devices may perceive slow signal fades that are due to the time-variant combination of several attenuated, delayed and phase shifted signal copies that traverse the multiple paths. In a different building, geometry may impact radio signals differently due to a different obstacle distribution, thus changing the radio performance for a similar deployment. Furthermore, in a different mounting location devices may receive radio signals differently as their antenna gain will depend on the orientation of antenna pattern main and side lobes with regards to the incident angle of radio signals. In addition, most buildings are occupied by moving people and obstacles (e.g., doors and windows) that can impact radio performance after installation. FIG. 7J shows a graph of received signal strength variability over time in which receiver devices perceive large and fast signal fades that can cause dynamic variations in a wave propagation path and invalidate an initial deployment plan.
Radio performance estimation techniques commonly employ building geometry and materials to predict wave propagation characteristics of wireless signals. (See EURASIP Journal on Wireless Communications and Networking 2009, 2009:415736 doi:10.1155/2009/415736 http://jwcn.eurasipjournals.com/content/2009/1/415736, which is incorporated herein by reference in its entirety.) Typical models can be constructed based on deterministic, empirical or hybrid methods. Deterministic models make use of the laws governing wave propagation to determine the received signal power at a particular location. Typical examples are the finite elements and ray tracing methods.
On the other hand, empirical models may employ probabilistic theory and data from previous measurement operations to predict signal power. Deterministic wave propagation models may be able to capture signal variance from building geometry by predicting small scale fading parameters, such as a delay profile. Empirical models may be able to capture signal variance from measuring received signals over a long time period and employing probabilistic regression models. Existing automated system planning solutions may employ average signal strength and represent temporal variability by means of a system specific link margin value. A high link margin value can increase the cost of the system, whereas a low value can limit radio connectivity, leading to poor system performance.
Building system sensors are also commonly battery-powered to provide low installation cost and a more flexible placement location. The energy supply for the building system sensors is limited and is usually consumed before end of a system's lifetime. Thus, battery consumption is another important sensor parameter for the building system performance estimation. In an empty building, battery consumption is mainly due to periodic monitoring operations, such as sensing and transmitting periodic radio heartbeats. However, for buildings that are occupied, battery depletion is dependent on several additional factors, such as number of re-transmissions required to successfully deliver radio heartbeats, frequency of events that are being detected, and frequency of alarm communication. Battery lifetime prediction models commonly account for only average sensor communication, processing and sensing requirements (see http://dl.acm.org/citation.cfm?id=1031518, which is incorporated herein by reference in its entirety). At-site tuning of battery lifetime prediction models that accounts for the above mentioned site and location specific factors can be a time consuming task for an installer. To compensate for a lack of at-site tuning, installer sensor lifetime estimations may be limited to common events that occur during a system's lifetime (e.g., expected number of card swipes in a battery-powered locking system).